



'''simple docstring'''





from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


SCREAMING_SNAKE_CASE__          =  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__          =  TypeVar("DatasetType", Dataset, IterableDataset)




def 							lowerCamelCase	(					_snake_case	:    List[DatasetType]  ,_snake_case	:    Optional[List[float]] = None  ,_snake_case	:    Optional[int] = None  ,_snake_case	:    Optional[DatasetInfo] = None  ,_snake_case	:    Optional[NamedSplit] = None  ,_snake_case	:    Literal["first_exhausted", "all_exhausted"] = "first_exhausted"  ,):





    '''simple docstring'''


    from .arrow_dataset import Dataset
    from .iterable_dataset import IterableDataset

    if not datasets:
        raise ValueError("Unable to interleave an empty list of datasets."							)
    for i, dataset in enumerate(_A							):
        if not isinstance(_A  ,(Dataset, IterableDataset)							):
            if isinstance(_A  ,(DatasetDict, IterableDatasetDict)							):
                if not dataset:
                    raise ValueError(
                        f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
                        "is an empty dataset dictionary."							)
                raise ValueError(
                    f'''Dataset at position {i} has at least one split: {list(_A							)}\n'''
                    f'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(_A							)							)}\']'''							)
            raise ValueError(
                f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_A							).__name__}.'''							)
        if i == 0:
            lowercase__							,							lowercase__									=    (
                (Dataset, IterableDataset) if isinstance(_A  ,_A							) else (IterableDataset, Dataset)
            )
        elif not isinstance(_A  ,_A							):
            raise ValueError(
                f'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'''							)
    if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
        raise ValueError(f'''{stopping_strategy} is not supported. Please enter a valid stopping_strategy.'''							)
    if dataset_type is Dataset:
        return _interleave_map_style_datasets(
            _A  ,_A  ,_A  ,info=_A  ,split=_A  ,stopping_strategy=_A							)
    else:
        return _interleave_iterable_datasets(
            _A  ,_A  ,_A  ,info=_A  ,split=_A  ,stopping_strategy=_A							)




def 							lowerCamelCase	(					_snake_case	:    List[DatasetType]  ,_snake_case	:    Optional[DatasetInfo] = None  ,_snake_case	:    Optional[NamedSplit] = None  ,_snake_case	:    int = 0  ,):





    '''simple docstring'''


    if not dsets:
        raise ValueError("Unable to concatenate an empty list of datasets."							)
    for i, dataset in enumerate(_A							):
        if not isinstance(_A  ,(Dataset, IterableDataset)							):
            if isinstance(_A  ,(DatasetDict, IterableDatasetDict)							):
                if not dataset:
                    raise ValueError(
                        f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
                        "is an empty dataset dictionary."							)
                raise ValueError(
                    f'''Dataset at position {i} has at least one split: {list(_A							)}\n'''
                    f'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(_A							)							)}\']'''							)
            raise ValueError(
                f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_A							).__name__}.'''							)
        if i == 0:
            lowercase__							,							lowercase__									=    (
                (Dataset, IterableDataset) if isinstance(_A  ,_A							) else (IterableDataset, Dataset)
            )
        elif not isinstance(_A  ,_A							):
            raise ValueError(
                f'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'''							)
    if dataset_type is Dataset:
        return _concatenate_map_style_datasets(_A  ,info=_A  ,split=_A  ,axis=_A							)
    else:
        return _concatenate_iterable_datasets(_A  ,info=_A  ,split=_A  ,axis=_A							)








import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class 							lowerCamelCase__						(				UpperCAmelCase       ,						unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       List[str] 			=		MvpTokenizer
			_UpperCamelCase		:       Any 			=		MvpTokenizerFast
			_UpperCamelCase		:       List[str] 			=		True
			_UpperCamelCase		:       Dict 			=		filter_roberta_detectors




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().setUp()
						UpperCamelCase__       =							[
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "\u0120",
						    "\u0120l",
						    "\u0120n",
						    "\u0120lo",
						    "\u0120low",
						    "er",
						    "\u0120lowest",
						    "\u0120newer",
						    "\u0120wider",
						    "<unk>",
						]
						UpperCamelCase__       =							dict(zip(snake_case   ,    range(len(snake_case  )  )  )  )
						UpperCamelCase__       =							["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
						UpperCamelCase__       =							{"unk_token": "<unk>"}

						UpperCamelCase__       =							os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES["vocab_file"]  )
						UpperCamelCase__       =							os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES["merges_file"]  )
						with open(self.vocab_file   ,    "w"   ,    encoding="utf-8"  ) as fp:
									fp.write(json.dumps(snake_case  ) + "\n"  )
						with open(self.merges_file   ,    "w"   ,    encoding="utf-8"  ) as fp:
									fp.write("\n".join(snake_case  )  )




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						kwargs.update(self.special_tokens_map  )
						return self.tokenizer_class.from_pretrained(self.tmpdirname   ,    **snake_case  )




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						kwargs.update(self.special_tokens_map  )
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,    **snake_case  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						return "lower newer", "lower newer"




			@cached_property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return MvpTokenizer.from_pretrained("RUCAIBox/mvp"  )




			@cached_property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							["A long paragraph for summarization.", "Another paragraph for summarization."]
						UpperCamelCase__       =							[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(snake_case   ,    max_length=len(snake_case  )   ,    padding=snake_case   ,    return_tensors="pt"  )
									self.assertIsInstance(snake_case   ,    snake_case  )

									self.assertEqual((2, 9)   ,    batch.input_ids.shape  )
									self.assertEqual((2, 9)   ,    batch.attention_mask.shape  )
									UpperCamelCase__       =							batch.input_ids.tolist()[0]
									self.assertListEqual(snake_case   ,    snake_case  )
									# Test that special tokens are reset




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							["A long paragraph for summarization.", "Another paragraph for summarization."]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(snake_case   ,    padding=snake_case   ,    return_tensors="pt"  )
									# check if input_ids are returned and no labels
									self.assertIn("input_ids"   ,    snake_case  )
									self.assertIn("attention_mask"   ,    snake_case  )
									self.assertNotIn("labels"   ,    snake_case  )
									self.assertNotIn("decoder_attention_mask"   ,    snake_case  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							[
						    "Summary of the text.",
						    "Another summary.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(text_target=snake_case   ,    max_length=32   ,    padding="max_length"   ,    return_tensors="pt"  )
									self.assertEqual(32   ,    targets["input_ids"].shape[1]  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(
									    ["I am a small frog" * 1024, "I am a small frog"]   ,    padding=snake_case   ,    truncation=snake_case   ,    return_tensors="pt"  )
									self.assertIsInstance(snake_case   ,    snake_case  )
									self.assertEqual(batch.input_ids.shape   ,    (2, 1024)  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							["A long paragraph for summarization."]
						UpperCamelCase__       =							[
						    "Summary of the text.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(snake_case   ,    text_target=snake_case   ,    return_tensors="pt"  )
									UpperCamelCase__       =							inputs["input_ids"]
									UpperCamelCase__       =							inputs["labels"]
									self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
									self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
									self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
									self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						pass







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
												UpperCamelCase__       =							self.rust_tokenizer_class.from_pretrained(snake_case   ,    **snake_case  )
												UpperCamelCase__       =							self.tokenizer_class.from_pretrained(snake_case   ,    **snake_case  )
												UpperCamelCase__       =							"A, <mask> AllenNLP sentence."
												UpperCamelCase__       =							tokenizer_r.encode_plus(snake_case   ,    add_special_tokens=snake_case   ,    return_token_type_ids=snake_case  )
												UpperCamelCase__       =							tokenizer_p.encode_plus(snake_case   ,    add_special_tokens=snake_case   ,    return_token_type_ids=snake_case  )

												# token_type_ids should put 0 everywhere
												self.assertEqual(sum(tokens_r["token_type_ids"]  )   ,    sum(tokens_p["token_type_ids"]  )  )

												# attention_mask should put 1 everywhere, so sum over length should be 1
												self.assertEqual(
												    sum(tokens_r["attention_mask"]  ) / len(tokens_r["attention_mask"]  )   ,    sum(tokens_p["attention_mask"]  ) / len(tokens_p["attention_mask"]  )   ,    )

												UpperCamelCase__       =							tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]  )
												UpperCamelCase__       =							tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]  )

												# Rust correctly handles the space before the mask while python doesnt
												self.assertSequenceEqual(tokens_p["input_ids"]   ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]  )
												self.assertSequenceEqual(tokens_r["input_ids"]   ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]  )

												self.assertSequenceEqual(
												    snake_case   ,    ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )
												self.assertSequenceEqual(
												    snake_case   ,    ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )


